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Mathematical Problems in Engineering
Volume 2014, Article ID 310478, 14 pages
Research Article

Variation-Oriented Data Filtering for Improvement in Model Complexity of Air Pollutant Prediction Model

1Department of Computer and Information Science, University of Macau, Macau
2Supporting Group, Faculty of Science and Technology, University of Macau, Macau
3Department of Electromechanical Engineering, University of Macau, Macau

Received 9 January 2014; Accepted 5 March 2014; Published 9 April 2014

Academic Editor: Qingsong Xu

Copyright © 2014 Chi Man Vong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate prediction models for air pollutants are crucial for forecast and health alarm to local inhabitants. In recent literature, discrete wavelet transform (DWT) was employed to decompose a series of air pollutant levels, followed by modeling using support vector machine (SVM). This combination of DWT and SVM was reported to produce a more accurate prediction model for air pollutants by investigating different levels of frequency bands. However, DWT has a significant demand in model complexity, namely, the training time and the model size of the prediction model. In this paper, a new method called variation-oriented filtering (VF) is proposed to remove the data with low variation, which can be considered as noise to a prediction model. By VF, the noise and the size of the series of air pollutant levels can be reduced simultaneously and hence so are the training time and model size. The SO2 (sulfur dioxide) level in Macau was selected as a test case. Experimental results show that VF can effectively and efficiently reduce the model complexity with improvement in predictive accuracy.